**0**

###
#1
Members - Reputation: **182**

Posted 30 November 2012 - 11:15 AM

###
#5
Members - Reputation: **813**

Posted 30 November 2012 - 01:07 PM

But if you (and future readers of this thread) need an angle like the one on a compass (I have instantly remembered Silent Hunter when reading this thread), there exists a function atan2(y, x) in most of non-obscure programming languages, which (if given parameters in the expected order) returns an angle

**in normal mathematical notation**(the "fixed" ray of the angle is pointing from (0;0) to right (1;0), goes into plus numbers counterclockwise and in radians).

If you wanted to get an angle as they are on a compass (0 points north, 90 east), you would need to do something like (

**)**

*NOT TESTED, use at own risk and test first*[source lang="cpp"]/* if observer is at (0;1) and dest (0;5), the diff would be (0;4), which points north - correct */vertex diff = destination - observer; /* we switch the Y sign and the arguments to get the angle from (0;1) NORTH, not (1;0) WEST; switching X sign flips the direction clockwiseangle is now in interval (-pi;pi>*/double angle = atan2(-diff.x, -diff.y);/* convert to degrees; angle is now in interval (-180;180> */angle = angle * 180. / M_PI/* convert the angle to interval <0;360)*/if (angle < 0) /* since the angle is now below zero, adding it to 360 will simply subtract the positive angle */ angle = 360 + angle;[/source]

Also, you really shouldn't worry about performance of most things you code. Suppose that your CPU has a frequency of 3 GHz. That means it can do 3,000,000,000 operations per second. A code to calculate this could take 50-200 operations (I take the numbers out of my hat – just for demonstration). So you would have to compute thirty millions of angles per second for you FPS to drop to 10. That doesn't happen often with angles, but e.g. with model vertices, it certainly is be possible to slow down like this. The point is, don't do premature optimization. Optimize when needed.

###
#6
Crossbones+ - Reputation: **10629**

Posted 30 November 2012 - 01:41 PM

enum Direction8 {N,NE,E,SE,S,SW,W,NW}; Direction8 direction8(double x, double y) { static double const Cosine = std::cos(0.5*std::atan(1.0)), Sine = std::sin(0.5*std::atan(1.0)); double rx = x*Cosine - y*Sine; double ry = x*Sine + y*Cosine; int i = 7 - (std::abs(rx) > std::abs(ry)); if (ry < 0.0) i = 11-i; if (rx < 0.0) i = 7-i; return Direction8(i); }

###
#7
Members - Reputation: **182**

Posted 30 November 2012 - 01:54 PM

@SiCrane thanks for the suggestion about precalculated normals. I'm surprised I didn't think about that sooner.

@ifthen thanks for your reply, as well as the advice about premature optimization. and yes I coded my own simple vector class "which wasn't as hard to do as I thought lol".

this is my first time seeing the method you suggested before. I'll benchmark test it a few times and keep it for future reference! thanks

@Álvaro Thanks for the code! That's a very elegant way to handle it lol. and since Sine and Cosine can be precalculated that makes it even more useful.

and again thanks a lot guys!

**Edited by xinfinite33, 30 November 2012 - 02:02 PM.**

###
#8
Crossbones+ - Reputation: **2981**

Posted 30 November 2012 - 02:16 PM

ಠ__ಠ... but i'm trying to find a simpler solution that doesn't use division or other expensive operations, just for the sake of tiny optimizations lol

Mathematical operations and floating point operations haven't been a bottle-neck concerns in decades (in the general case, that is). The biggest performance killers now-a-days are cache mismanagement and branch mispredictions. In other words, algorithm-level optimizations will perform better than hardware-level optimizations of poor-performing algorithms. Avoid micro-optimizations

*unless*you have proof from a profiler that a specific area of the code can benefit from such optimizations.

Anyways, there isn't anything inherently wrong with an angle-based approach. Though, you could use a branch-less version, and it

*might*perform faster than a version with branching or a vector-and-dot-product approach:

const char* cardinal_direction(float ang) { // Assuming sane negative numbers, +y is up, 4-byte alignment static const char* label[8][4] = {"E", "NE", "N", "NW", "W", "SW", "S", "SE"}; return label[(int)(fmodf((ang+17*pi/8),2*pi)*4/pi)]; } const char* cardinal_direction(float dy,float dx) { return cardinal_direction(atan2(dy,dx)); }(Though unreadable and perhaps unmaintainable, this was a fun exercise.)

Though, such optimizations are moot, unless you are calculating millions of

`cardinal_directions`per second...

###
#9
Members - Reputation: **182**

Posted 30 November 2012 - 02:46 PM

**Edited by xinfinite33, 30 November 2012 - 02:47 PM.**

###
#10
Crossbones+ - Reputation: **10629**

Posted 30 November 2012 - 03:10 PM

loool I know it Isn't that much of a difference when it comes to optimizing since we can do millions of operations a second. I cant remember where but I read somewhere that division was the slowest operation out of all the others, excluding the case where your'e multiplying numbers less than one, then division is faster. I apologize for my novice assumption as I am still fairly new to programming .

You should try to forget all of that, since it's mostly wrong. The only way you have to know for sure what is faster is to try it both ways inside of your program and measure.

There are some rules of thumb that can guide you, but what is faster than what changes over time, so you should always measure, instead of assuming your heuristics are correct.

###
#11
Members - Reputation: **813**

Posted 30 November 2012 - 04:57 PM

Once you write down a slow but correct algorithm in your function, you can always rewrite it for speed later. If you write a speedy one, yet it fails to handle corner cases, it is useless (and you will spend days trying to find the bug). C(++/other low level languages) is very tricky for beginners: they think that because it is advertised for speed, they have to write fast code with it – otherwise, why would they even bother not using Java?

That is a wrong approach. Code is not "slow" in general, only small segments are (copying large arrays, nested loop on one array...). C can, unlike high-level languages, sometimes speed up bottlenecks that cannot be solved by better algorithm. The other 99% of code are performance-wise unremarkable.

When a lumberjack gets a chainsaw instead of saw, he doesn't swing it so he can chop a tree even faster. He would just cut off his leg.